Weka开发［6］－LibSVM

       首先要提的是LibSVM是一个库，Lib很明显是library的缩写，有些人不知道怎么会认为它是一种算法。它是由中国台湾的，记住是中国的台湾(⊙o⊙)…，Chih-Chung Chang和Chih-Jen Lin等人开发的，他们用多种语言实现写了LibSVM。

       我把这一篇放到Weka开发里讲，主要讲它怎么和Weka结合，Weka中并不是没有SVM算法，Weka中有SMO算法的实现。

 

Weka and LibSVM are two efficient software tools for building SVM classifiers. Each one of these two tools has its points of strength and weakness. Weka has a GUI and produces many useful statistics (e.g. confusion matrix, precision, recall, F-measure, and ROC scores). LibSVM runs much faster than Weka SMO and supports several SVM methods (e.g. One-class SVM, nu-SVM, and R-SVM). Weka LibSVM (WLSVM) combines the merits of the two tools. WLSVM can be viewed as an implementation of the LibSVM running under Weka environment.

 

这一段是我从拷贝来的，请注意里面有一句话，LibSVM运行的比Weka里的SMO快的多，如果你敢用SMO算法去训练大数据集，你就明白天荒地老的真实含意了。其它和Weka结合的最主要的原因，我认为是，我们开始的时候往往都是用别的算法去试着做实验的（或者本来就需要多种基分类器），比如Naïve Bayes(LibSVM是比SMO快的多，可是比起来Naïve Bayes，它还是蜗牛)，到最后又想试试LibSVM，这时就需要LibSVM库。

还有一点也是容易误解的就是Weka里的高版本，里面是有LibSVM这个分类器，但你如果直接想运行是会出错的，提示你没有设置路径。原因是：WLSVM can be viewed as an implementation of the LibSVM running under Weka environment.

先把LibSVM下载下来，上次竟然有人问我在哪下载，这种问题，我真是不想回答，不就是google一下吗？网址也放上：http://www.csie.ntu.edu.tw/~cjlin/libsvm/，下载WLSVM，解压后Lib文件夹下有一个LibSVM.jar的包，用和导入Weka.jar包相同的方式导入就好了，然后使用LibSVM和使用以前任何一种分类器的方式都是一样的。这里顺便带大家复习以下特征选择。

package com.cizito.weka.study;

import java.util.Random;

import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.LibLINEAR;
import weka.classifiers.functions.LibSVM;
import weka.classifiers.meta.AttributeSelectedClassifier;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.supervised.attribute.AttributeSelection;

/**
 * @author zhangwei
 *
 */
public class LibSVMTest {

	private Instances m_instances = null;
	private Instances selectedIns;
	
	
	public static void main( String[] args ) throws Exception {
		LibSVMTest filter = new LibSVMTest();
	       
	        filter.getFileInstances( "D:/ProgramFiles/Weka-3-6/data/soybean.arff");
	        filter.selectAttUseFilter();
	       
	        filter.selectAttUseMC();
	}
	   
    public void getFileInstances( String fileName ) throws Exception {
        DataSource frData = new DataSource( fileName );
        m_instances = frData.getDataSet();
       
        m_instances.setClassIndex( m_instances.numAttributes() - 1 );
    }
   
    public void selectAttUseFilter() throws Exception {
        AttributeSelection filter = new AttributeSelection();  // package weka.filters.supervised.attribute!
        CfsSubsetEval eval = new CfsSubsetEval();
        GreedyStepwise search = new GreedyStepwise();
        filter.setEvaluator(eval);
        filter.setSearch(search);
        filter.setInputFormat( m_instances );
       
        System.out.println( "number of instance attribute = " + m_instances.numAttributes() );
       
        selectedIns = Filter.useFilter( m_instances, filter);
        System.out.println( "number of selected instance attribute = " + selectedIns.numAttributes() );

        for( int i = 0; i < selectedIns.numInstances(); i++ ) {
           
            System.out.println( selectedIns.instance( i ) );
        }
    }
   
    public void selectAttUseMC() throws Exception {  
         AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
         CfsSubsetEval eval = new CfsSubsetEval();
         GreedyStepwise search = new GreedyStepwise();
         J48 base = new J48();
         NaiveBayes nb = new NaiveBayes();
         LibSVM svm = new LibSVM();
         LibLINEAR linear = new LibLINEAR();
         classifier.setClassifier( svm );
         classifier.setEvaluator( eval );
         classifier.setSearch( search );
         // 10-fold cross-validation
         Evaluation evaluation = new Evaluation( selectedIns );
         evaluation.crossValidateModel(svm, m_instances, 10, new Random(1));
         System.out.println("正确率为：" + (1-evaluation.errorRate()));
         System.out.println( evaluation.toSummaryString());
        
    }

}

   好人做到底吧，顺便把LibSVM包上传，然后告诉大家其实新版的WEKA里面已经包含了LibSVM这个分类器了